L11 Themes

Overview

The goal of this lab is to play around with the theme options in ggplot2

Datasets

We’ll be using the cdc.txt, mod_nba2014_15_advanced.txt, and NU_admission_data.csv datasets.

# Load package(s)
library(tidyverse)
library(patchwork)

# Read in the cdc dataset
cdc <- read_delim(file = "data/cdc.txt", delim = "|") %>%
  mutate(genhlth = factor(genhlth,
    levels = c("excellent", "very good", "good", "fair", "poor"),
    labels = c("Excellent", "Very Good", "Good", "Fair", "Poor")
  ))

# Read in nba data
nba_dat <- read_delim(
    file = "data/mod_nba2014_15_advanced.txt",
    delim = "|"
  ) %>%
  janitor::clean_names()

# Read in NU admission data


# Set seed
set.seed(2468)

# Selecting a random subset of size 100
cdc_small <- cdc %>% slice_sample(n = 100)

Exercises

Complete the following exercises.

Exercise 1

Use the cdc_small dataset to explore several pre-set ggthemes. The code below constructs the familiar scatterplot of weight by height and stores it in plot_01. Display plot_01 to observe the default theme. Explore/apply, and display at least 7 other pre-set themes from the ggplot2 or ggthemes package. Don’t worry about making adjustments to the figures under the new themes. Just get a sense of what the themes are doing to the original figure plot_01.

Which theme or themes do you particularly like? Why?

There should be at least 8 plots for this task. temp1 is pictured below. Would suggest using patchwork or cowplot in combination with R chunk options (fig.height & fig.width) to setup the 8 plots in a more user friendly arrangement.

# Building plot
plot_01 <- ggplot(data = cdc_small, aes(x = height, y = weight)) +
  geom_point(size = 3, aes(shape = genhlth, color = genhlth)) +
  scale_y_continuous(
    name = "Weight in Pounds",
    limits = c(100, 275),
    breaks = seq(100, 275, 25),
    trans = "log10",
    labels = scales::label_number(accuracy = 1, suffix = " lbs")
  ) +
  scale_x_continuous(
    name = "Height in Inches",
    limits = c(60, 80),
    breaks = seq(60, 80, 5),
    labels = scales::label_number(accuracy = 1, suffix = " in")
  ) +
  scale_shape_manual(
    name = "Health?",
    labels = c("Excellent", "Very Good", "Good", "Fair", "Poor"),
    values = c(17, 19, 15, 9, 4)
  ) +
  scale_color_brewer(
    name = "Health?",
    labels = c("Excellent", "Very Good", "Good", "Fair", "Poor"),
    palette = "Set1"
  ) +
  theme(
    legend.position = c(1, 0),
    legend.justification = c(1, 0)
  ) +
  labs(title = "CDC BRFSS: Weight by Height")

Answer: I particularly like theme_classic() and theme_tufte() since these remind me of book-style graphs.

# plots
plot_02 <- plot_01 +
  theme_bw()

plot_03 <- plot_01 +
  theme_linedraw()

plot_04 <- plot_01 +
  theme_light()

plot_05 <- plot_01 +
  theme_dark()

plot_06 <- plot_01 +
  theme_minimal()

plot_07 <- plot_01 +
  theme_classic()

plot_08 <- plot_01 +
  ggthemes::theme_solarized()

plot_09 <- plot_01 +
  ggthemes::theme_tufte()

plot_01

plot_02

plot_03

plot_04

plot_05

plot_06

plot_07

plot_08

plot_09

Exercise 2

Using plot_01 from and the theme() function, attempt to construct the ugliest plot possible (example pictured below).

Answer:

# plot
plot_01 +
  theme(
    plot.background = element_rect(fill = "hot pink", colour = NA),
    panel.grid.major.x = element_line(color = "pink", colour = NA, size = 1.5, linetype = "dashed"),
    panel.grid.major.y = element_line(color = "pink", colour = NA, size = 1.5, linetype = "dashed"),
    panel.grid.minor = element_line(color = "blue", colour = NA, size = 1.5),
    legend.background = element_rect(fill = "tan"),
    axis.ticks.x = element_line(color = "yellow", size = 2),
    legend.text = element_text(color = "brown"),
    legend.title = element_text(color = "blue"),
    axis.text.x = element_text(angle = 340, color = "red"),
    axis.text.y = element_text(angle = 180, color = "blue"),
    axis.title.x = element_text(color = "red"),
    axis.title.y = element_text(color = "blue"),
    title = element_text(color = "purple")
  )

Exercise 3

Using the mod_nba2014_15.txt dataset we will finish the process of recreating/approximating the plot type featured in the http://fivethirtyeight.com/ article Kawhi Leonard Is The Most Well-Rounded Elite Shooter Since Larry Bird for any player of your choice for the 2014-2015 season.

In L09 Coordinates we did the data wrangling (directly below) and partially built the desired plot (in the next r chunk).

## Helper function
# Returns quartile rank
quartile_rank <- function(x = 0:99) {
  # Set quartile
  quart_breaks <- c(
    -Inf,
    quantile(x,
      probs = c(.25, .5, .75),
      na.rm = TRUE
    ),
    Inf
  )
  cut(x = x, breaks = quart_breaks, labels = FALSE)
}

# Building graphing data
nba_graph_dat <- nba_dat %>% 
  filter(g >= 10, mp / g >= 5) %>% 
  mutate(
    ts_quant = quartile_rank(ts_perc),
    trb_quant = quartile_rank(trb_perc),
    dbpm_quant = quartile_rank(dbpm),
    ast_quant = quartile_rank(ast_perc),
    usg_quant = quartile_rank(usg_perc)
  ) %>% 
  select(player, contains("_quant")) %>% 
  # rename(
  #   "Rebound Rate" = trb_quant,
  #   "Defensive BPM" = dbpm_quant,
  #   "Assist Rate" = ast_quant,
  #   "True Shooting" = ts_quant,
  #   "Usage Rate" = usg_quant
  # ) %>% 
  pivot_longer(
    cols = -player, 
    names_to = "variable", 
    values_to = "value"
    ) %>% 
  arrange(player)

All that is left is some tweaking and theming.

Hints:

All added text, except title, is done with annotate().
Image width is 8 in, height is 10 in. Can be achieved many ways:
- Set figure height and width in R chuck’s options
- Save image with ggsave() and then insert it using markdown code:
  - ![Caption - leave blank](/path/to/image.png)
  - Set figure height and width in R chuck’s options and then use knitr::include_graphic(/path/to/image.png)

Set fig.width=8 and fig.height=10 to ensure device prints the graphic consistently. This ensures the aspect ratio and text size selections will be appropriate. Then set out.fig='50%' to shrink it down for display. Also set fig.align='center'.

# Select player
player_plot <- "LeBron James"

# Build plot
nba_graph_dat %>%
  filter(player == player_plot) %>%
  ggplot(aes(x = variable, y = value)) +
  geom_col(width = 1, fill = "#F28291") +
  scale_x_discrete(NULL,
    expand = c(0, 0),
    limits = c(
      "ts_quant", "usg_quant",
      "dbpm_quant", "trb_quant",
      "ast_quant"
    )
  ) +
  scale_y_continuous(NULL, expand = c(0, 0)) +
  # Adding dotted lines
  geom_hline(yintercept = 1:4, linetype = "dotted") +
  # Adding vertical lines
  geom_segment(x = 0.5:4.5, y = 0, xend = 0.5:4.5, yend = 4) +
  ggtitle(str_c(player_plot, "(2015)", sep = "\n")) +
  coord_polar() +
  theme(
    panel.grid = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    plot.title = element_text(hjust = 0.5, size = 24)
  ) +
  # Adding sector labels
  annotate(
    geom = "text", 
    label = c("TRUE\nSHOOTING", "USAGE\nRATE", "DEFENSIVE\nBPM", "REBOUND\nRATE", "ASSIST\nRATE"), 
    x = 1:5, 
    y = 5,
    size = 4
  ) +
  # Adding quartile labels
  annotate(
    geom = "text",
    label = c("1st-25th", "25th-50th", "50th-75th", "75th-99th"),
    x = 3, 
    y = 0.7:3.7, 
    size = 3
  )

Exercise 4

We will be making use of your code from exercise 3 from L08 Layers. Using the NU_admission_data.csv you created two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf. Style these plots so they follow a “Northwestern” theme. You are welcome to display the plots separately or design a layout that displays both together (likely one stacked above the other).

Check out the following webpages to help create your Northwestern theme:

Visual Identity

Color Palettes
Fonts & Typography — Need to use substitute fonts